Deep convolutional neural networks for automatic segmentation of left ventricle cavity from cardiac magnetic resonance images

Deep convolutional neural networks for automatic segmentation of left ventricle cavity from cardiac magnetic resonance images

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This work conducts a feasibility study of deep learning approaches for automatic segmentation of left ventricle (LV) cavity from cardiac magnetic resonance (CMR) images. Automatic LV cavity segmentation is a challenging task, partially due to the small size of the object as compared to the large CMR image background, especially at the apex. To cater for small object segmentation, the authors present a localisation-segmentation framework, to first locate the object in the large full image, then segment the object within the small cropped region of interest. The localisation is performed by a deep regression model based on convolutional neural networks, while the segmentation is done by the deep neural networks based on U-Net architecture. They also employ the Dice loss function for the training process of the segmentation models, to investigate its effects on the segmentation performance. The deep learning models are trained and evaluated by using public endocardium-annotated CMR datasets from York University and MICCAI 2009 LV Challenge websites. The average dice metric values of the authors’ proposed framework are 0.91 and 0.93, respectively, on these two databases. These results are promising as compared to the best results achieved by the current state-of-art, which shows the potentials of deep learning approaches for this particular application.


    1. 1)
      • 1. Petitjean, C., Dacher, J.N.: ‘A review of segmentation methods in short axis cardiac MR images’, Med. Image Anal., 2011, 15, (2), pp. 169184.
    2. 2)
      • 2. Kang, D., Woo, J., Slomka, P.J., et al: ‘Heart chambers and whole heart segmentation techniques: review’, SPIE J. Electron. Imag., 2012, 21, (1), pp. 131139.
    3. 3)
      • 3. Cocosco, C., Niessen, W., Netsch, T., et al: ‘Automatic image- driven segmentation of the ventricles in cardiac cine MRI’, J. Magn. Reson. Imag., 2008, 28, (2), pp. 366374.
    4. 4)
      • 4. Bengio, Y., Courville, A., Vincent, P.: ‘Representation learning: a review and new perspectives’, IEEE Trans. Pattern Anal. Mach. Intell., 2013, 35, (8), pp. 17982828.
    5. 5)
      • 5. LeCun, Y., Bengio, Y., Hinton, G.: ‘Deep learning’, Nature, 2015, 521, pp. 436444.
    6. 6)
      • 6. Schmidhuber, J.: ‘Deep learning in neural networks: an overview’, Neural Netw., 2015, 61, pp. 85117.
    7. 7)
      • 7. LeCun, Y., Bottou, L., Bengio, Y., et al: ‘Gradient-based learning applied to document recognition’, Proc. IEEE, 1998, 86, (11), pp. 22782324.
    8. 8)
      • 8. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ‘ImageNet classification with deep convolutional neural networks’. Neural Information Processing Systems (NIPS 2012), 2012, pp. 11061114.
    9. 9)
      • 9. Simonyan, K., Zisserman, A.: ‘Very deep convolutional networks for large-scale image recognition’, 2014. Available at
    10. 10)
      • 10. Szegedy, C., Liu, W., Jia, Y., et al: ‘Going deeper with convolutions’, 2014. Available at
    11. 11)
      • 11. He, K., Zhang, X., Ren, S., et al: ‘Deep residual learning for image recognition’, 2015. Available at
    12. 12)
      • 12. Ciresan, D.C., Giusti, A., Gambardella, L., et al: ‘Mitosis detection in breast cancer histology images with deep neural networks’. Medical Image Computing and Computer Assisted Interventions (MICCAI 2013), 2013 (LNCS, 8150), pp. 411418.
    13. 13)
      • 13. Malon, C., Cosatto, E.: ‘Classification of mitotic figures with convolutional neural networks and seeded blob features’, J. Pathol. Inf., 2013, 4, (1), p. 9.
    14. 14)
      • 14. Cruz-Roa, A., Basavanhally, A., Gonzalez, F., et al: ‘Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks’. SPIE Medical Imaging, vol. 9041, 2014, doi: 10.1117/12.2043872.
    15. 15)
      • 15. Cruz-Roa, A., Arevalo, J., Madabhushi, A., et al: ‘A deep learning architecture for image representation, visual interpretability and automated basal-cell carcinoma cancer detection’. Medical Image Computing and Computer-Assisted Intervention (MICCAI 2013), 2013 (LNCS, 8150), pp. 403410.
    16. 16)
      • 16. Ciresan, D.C., Giusti, A., Gambardella, L.M., et al: ‘Deep neural networks segment neuronal membranes in electron microscopy images’. Neural Information Processing Systems (NIPS 2012), 2012, pp. 28432851.
    17. 17)
      • 17. Esteva, A., Kuprel, B., Thrun, S.: ‘Deep networks for early stage skin disease and skin cancer classification’. Project Report, Stanford University, 2015.
    18. 18)
      • 18. Chen, T., Chefdhotel, C.: ‘Deep learning based automatic immune cell detection for immunohistochemistry images’. 5th Int. Workshop on Machine Learning in Medical Imaging (MLMI'14), Boston, MA, USA, 2014, pp. 1724.
    19. 19)
      • 19. Dhungel, N., Carneiro, G., Bradley, A.P.: ‘Deep learning and structured prediction for the segmentation of mass in mammograms’. Medical Image Computing and Computer Assisted Intervention (MICCAI 2015), 2015 (LNCS, 9349), pp. 605612.
    20. 20)
      • 20. Dhungel, N., Carneiro, G., Bradley, A.P.: ‘Deep structured learning for mass segmentation from mammograms’, 2014. Available at
    21. 21)
      • 21. Yang, X.L., Yeo, S.Y., Hong, J.M., et al: ‘A deep learning approach for tumor tissue image classification’. IASTED Biomedical Engineering, proceeding, 832, 2016, DOI: 10.2316/P.2016.832-025.
    22. 22)
      • 22. van Tulder, G., de Bruijne, M.: ‘Learning features for tissue classification with the classification restricted Boltzmann machine’. MCV, 2014 (LNCS, 8848), pp. 4758.
    23. 23)
      • 23. Greenspan, H., van Ginneken, B., Summers, R.M.: ‘Guest editorial – deep learning in medical imaging: overview and future promise of an exciting new techniques’, IEEE Trans. Med. Imag., 2016, 35, (5), pp. 11531159.
    24. 24)
      • 24. Avendi, M.R., Kheradvar, A., Jafarkhani, H.: ‘A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in Cardiac MRI, 2015. Available at
    25. 25)
      • 25. Emad, O., Yassine, I.A., Fahmy, A.S.: ‘Automatic localization of the left ventricle in cardiac MRI images using deep learning’. IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy, 2015, pp. 683686.
    26. 26)
      • 26. Ngo, T.A., Carneiro, G.: ‘Left ventricle segmentation from cardiac MRI combining level set methods with deep belief networks’. IEEE Int. Conf. Image Processing (ICIP), Melbourne, Australia, 2013, pp. 695699.
    27. 27)
      • 27. Long, J., Shelhamer, E., Darrell, T.: ‘Fully convolutional networks for semantic segmentation’, 2015. Available at
    28. 28)
      • 28. Tran, P.V.: ‘A fully convolutional neural network for cardiac segmentation in short-axis MRI’, 2016. Available at
    29. 29)
      • 29. Ronneberger, O., Fischer, P., Brox, T.: ‘U-Net: convolutional networks for biomedical image segmentation’, 2015. Available at
    30. 30)
      • 30. Yang, X.L., Like, G., Yeo, S.Y., et al: ‘Automatic segmentation of left ventricular myocardium by deep convolutional and de-convolutional neural networks’. Computing in Cardiology (CINC 2016), 2016, pp. 8184.
    31. 31)
      • 31. Milletari, F., Navab, N., Ahmadi, S.A.: ‘V-Net: fully convolutional neural networks for volumetric medical image segmentation’, 2016. Available at
    32. 32)
      • 32. Drozdzal, M., Vorontsov, E., Chartrand, G., et al: ‘The importance of skip connections in biomedical image segmentation’, 2016. Available at
    33. 33)
      • 33. Andreopoulos, A., Tsotsos, J.K.: ‘Efficient and generalizable statistical models of shape and appearance for analysis of cardiac MRI’, Med. Image Anal., 2008, 12, (3), pp. 335357. Available at
    34. 34)
      • 34. Radau, P., Lu, Y., Connelly, K., et al: ‘Evaluation framework for algorithms segmenting short axis cardiac MRI’. MICCAI 2009 Workshop on Cardiac MR Left Ventricle Segmentation Challenge, 2009. Available at
    35. 35)
      • 35. Li, Y., Qi, H., Dai, J., et al: ‘Fully convolutional instance-aware semantic segmentation’, 2016. Available at
    36. 36)
      • 36. He, K., Gkioxari, G., Dollar, P., et al: ‘Mask R-CNN’, 2017. Available at
    37. 37)
      • 37. Petitjean, C., Zuluaga, M.A., Bai, W., et al: ‘Right ventricle segmentation from cardiac MRI: a collation study’, Med. Image Anal., 2015, 19, (1), pp. 287202.
    38. 38)
      • 38. Yang, X.L., Su, Y., Duan, R., et al: ‘Cardiac image segmentation by random walks with dynamic information’, IET Comput. Vis., 2016, 10, (1), pp. 7986.
    39. 39)
      • 39. Noh, H., Hong, S., Han, B.: ‘Learning deconvolution network for semantic segmentation’, 2015. Available at
    40. 40)
      • 40. Chen, L.C., Papandreou, G., Kokkinos, I., et al: ‘Semantic image segmentation with deep convolutional nets and fully connected CRFs’, 2014. Available at
    41. 41)
      • 41. Zheng, S., Jayasumana, S., Romera-Paredes, B., et al: ‘Conditional random fields as recurrent neural networks’, 2015. Available at
    42. 42)
      • 42. Yu, F., Koltun, V.: ‘Multi-scale context aggregation by dilated convolutions’, 2015. Available at
    43. 43)
      • 43. U-Net Kera Implementation, 2016. Available at
    44. 44)
      • 44. Deep Learning for Cardiac Segmentation, 2017. Available at
    45. 45)
      • 45. Kingma, D., Ba, J.: ‘Adam: a method for stochastic optimization’, 2014. Available at
    46. 46)
      • 46. Kleesiek, J., Urban, G., Hubert, A., et al: ‘Deep MRI brain extraction: a 3D convolutional neural networks for skull stripping’, NeuroImage, 2016, 129, pp. 460469.
    47. 47)
      • 47. Korlev, S., Safiullin, A., Belyaev, M., et al: ‘Residual and plain convolutional neural networks for 3D brain MRI classification’, 2017. Available at

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